Cloning, Expression, Primary Structure, and Insertion Mutagenesis of the Pyruvate Formate-Lyase Enzyme of Aeromonas Hydrophila.

The pyruvate formate-lyase enzyme (Pfl), a key enzyme in alcoholic fermentation, catalyzes the anaerobic conversion of pyruvate into acetyl-CoA and formate in prokaryotes. The pyruvate formate-lyase gene (pfl) of the facultative anaerobe Aeromonas hydrophila was cloned and sequenced. Subsequently, the Pfl was characterized based on the deduced amino acid sequence and evaluated in vivo following insertion mutagenesis. Finally, the pathogenesis of a defined mutant was compared to wild type A. hydrophila in channel catfish. The pfl of A. hydrophila was identified from two recombinant pBluescript SK(-) clones excised from a $\lambda$ ZAP II phage library. Both clones carried the same 9.15 Kbp insert, oriented in opposite directions, and expressed two polypeptide of 85 & 82 KDa sizes regardless of isopropyl-B-D-thiogalactoside induction. Dideoxy sequencing of a 4.8 Kbp subclone expressing the two immunoreactive polypeptides identified a 2280 base open reading frame (ORF), with 70% sequence homology to the E. coli pfl. The ORF encodes an 85.5 KDa protein containing 760 amino acid residues with 85.5% homology to the E. coli Pfl peptide sequence. The Aeromonas Pfl secondary structure consists of 46% $\alpha$-helices and 39% $\beta$-sheets. Catalytically important adjacent cysteine residues in E. coli Pfl at positions 419-420 were also located at the same positions in the A. hydrophila Pfl. A free radical identified at glycine-735 residue in the cloned enzyme was also in the same position as in E. coli Pfl. A candidate trypsin cleavage site, located between arginine residues at 624-625 positions in A. hydrophila Pfl, was confirmed by the presence of a 68 KDa trypsin resistant fragment. Insertion mutagenesis of the pfl with a kanamycin resistant marker (kan) resulted in isolating an obligate aerobic A. hydrophila pfl mutant strain, which was characterized phenotypically by anaerobic growth on pyruvate and antibiotic resistance profile. Polymerase chain reaction experiments confirmed the insertion of the kan marker into the mutant chromosome and the occurrence of a double-crossover homologous recombination event. No significant difference was noticed in the LD\sb{50} in channel catfish injected with either the parent or the mutant strain, indicating that the Pfl may not play an important role in the pathogenesis of acute A. hydrophila infections

On unit P-Groups in Group Algebra

Abstract:The aim of this paper we have define the group of units U(F(G)), where F(G) is the group algebra with G is finite group over a field F. Now if char F=0 and G nonabelian or F is a nonabsolute field of characterstic > 0 and G/ O (G) is nonabelian, then it is well known that the group of unit U(K[G]) contains a nonabelain P-group.There for we will prove that there are two cyclic subgroups X and Y of G of prime power order and units uX U(K[X]) and uY U(K[X]) such that (uX,uY) contain nonabelian P-subgroups in linear grou

Position-based routing algorithms for three-dimensional ad hoc networks

In position-based routing algorithms, the nodes use the geographical information to make routing decisions. Recent research in this field addresses such routing algorithms in two-dimensional (2 D ) space. However, in real applications, the nodes may be distributed in three-dimensional (3 D ) space. Transition from 2 D to 3 D is not always easy, since many problems in 3 D are significantly harder than their 2 D counterparts. This dissertation focuses on providing a reliable and efficient position-based routing algorithms with the associated pre-processing algorithms for various 3 D ad hoc networks. In the first part of this thesis, we propose a generalization of the Yao graph where the cones used are adaptively centered on the nearest set of neighbors for each node, thus creating a directed or undirected spanning subgraph of a given unit disk graph (UDG). We show that these locally constructed spanning subgraphs are strongly connected, have bounded out-degree, are t -spanners with bounded stretch factor, contain the Euclidean minimum spanning tree as a subgraph, and are orientation-invariant. Then we propose the first local, constant time algorithm that constructs an independent dominating set and connected dominating set of a Unit Disk Graph in a 3 D environment. We present a truncated octahedral tiling system of the space to assign to each node a class number depending on the position of the node within the tiling system. Then, based on the tiling system, we present our local algorithms for constructing the dominating sets. The new algorithms have a constant time complexity and have approximation bounds that are completely independent of the size of the network. In the second part of this thesis, we implement 3 D versions of many current 2 D position-based routing algorithms in addition to creating many new algorithms that are specially designed for a 3 D environment. We show experimentally that these new routing algorithms can achieve nearly guaranteed delivery while discovering routes significantly closer in length to a shortest path. Because many existing position-based routing algorithms for ad hoc and sensor networks use the maximum transmission power of the nodes to discover neighbors, which is a very power-consuming process. We propose several localized power-aware 3 D position-based routing algorithms that increase the lifetime of a network by maximizing the average lifetime of its nodes. These new algorithms use the idea of replacing the constant transmission power of a node with an adjusted transmission power during two stages. The simulation results show a significant improvement in the overall network lifetime over the current power-aware routing algorithm

Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data

Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies

Association of ADH1 and DDR48 Expression with Azole Resistance in Candida Albicans

Objectives: The ADH1 (alcohol dehydrogenase) and DDR48 genes were found to be upregulated in fluconazole resistant Candida albicans. Understanding the drug resistance mechanisms and genes will help in the development of new antifungal agents that can reverse drug resistance. This study aimed to investigate the role of ADH1and DDR48 genes in fluconazole resistance in C. albicans.Methods: This study involved 19 fluconazole susceptible and 6 fluconazole resistant C. albicans isolates. The MICs of fluconazole were determined by the E-test. Quantitative expressions of ADH1, CDR1 (Candida Drug Resistance), DDR48 and FLU1 genes were assessed by real time PCR.Results: There was a statistically significant higher expression levels of CDR1, FLU1, ADH1 and DDR48 in resistant and susceptible dose dependent isolates than in susceptible isolates (P = 0.009, 0.008, 0.01, 0.014 respectively). Strong positive correlations were observed between the expression levels of each of ADH1and DDR4 with azole resistance genes CDR1 and FLU1 [(rs) = 0.945, 0.815, respectively; P <0.001; (rs) = 0.852 and 0.76, respectively; P <0.001].Conclusions: This is the first study that showed positive correlation between DDR48 and azole resistance genes. It indicated that ADH1 and DDR48 are associated with the resistance mechanisms of C. albicans to fluconazole. Identification of new drugs that target the proteins encoded by these genes will help in eradication of fluconazole resistant C. albicans

ON DIFFERENTIAL SUBORDINATIONS CONNECTED WITH BAZILOEVIC FUNCTIONS RELATED TO A SECTOR.

In this investigation we generalized the class introduced by J. patel [7] of multivalent analytic functions to the subclass which has been recently published. Also we derive various properties by using the techniques of Briat-Bouquet differential subordination

Universal Data Logger System for Environmental Monitoring Applications

Collecting huge amount of data in long time acquisition systems like in environmental monitoring, there is a need to collect and save data over time for further use or analysis. A data logger is an electronic device that records data over time or in a relation to location either with a built-in instrument, sensor or via an external instruments and sensor. In this paper, a data logger system is designed to use as a stand-alone or computer based device. When used as a standalone system, all data acquired are saved in SD memory card, which must be copied and erased periodically depending on the memory size. When used as computer based device, all the data sent to the computer via the serial port and stored automatically in achieved files. The limit of those files size only restricted by the capacity of the disk. The data logger is designed using an Arduino UNO board and LabView software, and it has the flexibility to set it up for different user options. With this system, the user could be able to record and read back sensory data to or from existing files, or in automatically generated files and plot these readings in a graph. Also, the user have the ability to choose the periodic time at which a sample record in a file in term of seconds, minutes or hours. The system designed to monitor and record a single channel data, but it could be adapted to monitor more than one channel

COVID-19 associated mucormycosis and diabetes mellitus: An exploratory study

Background:Mucormycosis has abruptly increased in Egypt during the third wave of COVID-19 especially in patients with diabetes mellitus (DM). The aim of this study was to investigate the risk factors, clinical presentation and outcome of mucormycosis in COVID-19 patients with diabetes. Methods: Prospective cohort study was conducted on 72 COVID-19 patients with DM presented with mucormycosis at intensive care units and Ear, Nose, and Throat Department of Zagazig University Hospitals over a period of three months from May 2021 to August 2021. All participants were submitted to history taking, examination, laboratory investigation, radiological and histopathology and culture testing. Results: Post COVID-19 new-onset diabetes mellitus (NOD) was detected in 40% of studied patients. 72.2% of patients had poorly controlled diabetes. Majority of studied patients presented by rhino-orbital mucormycosis (90.3%) and about 86% of them were operated. Hundred percent of patients gave history of antibiotic use and also nearly 99.0% of them received corticosteroids, while only 1.4% of them received tocilizumab. There was statistically significant association between operated patients, hemoglobin (HB) level, lymphocyte count, neutrophil-lymphocyte ratio (NLR), and CRP level with disease prognosis. Conclusions: Poorly controlled DM and steroid use are the most important risk for post COVID-19 mucormycosis. Early surgical intervention carried better disease outcome

Active Learning with Noisy Labelers for Improving Classification Accuracy of Connected Vehicles

Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. Reacting to such situations requires accurate classification for uncommon events, which in turn depends on the selection of large, diverse, and high-quality training data. In fact, the data available at a vehicle (e.g., photos of road signs) may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. Given the information received from neighboring vehicles, our solution: (i) selects which vehicles can reliably generate high-quality training data, and (ii) obtains a reliable subset of data to add to the training set by trading off between two essential features, i.e., quality and diversity. The results, obtained with different real-world datasets, demonstrate that our framework significantly outperforms state-of-the-art solutions, providing high classification accuracy with a limited bandwidth requirement for the data exchange between vehicles